<!DOCTYPE html>
<html>
<head>

  <!-- Mock -->
  <meta charset="utf-8" />
  <meta http-equiv="X-UA-Compatible" content="IE=edge" />

  <title>Glow: Better Reversible Generative Models</title>

  <meta name="HandheldFriendly" content="True" />
  <meta name="viewport" content="width=device-width, initial-scale=1.0" />

  <link href="https://fonts.googleapis.com/css?family=Lato:400,400i,700"
    rel="stylesheet"> 
  <link href="mock.css" rel="stylesheet">

  <!-- GlowDemo Assets -->
  <!-- dependency: jQuery -->
  <script
    src="https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js">
    </script>
  <!-- dependency: JavaScript-Load-Image -->
  <script src="load-image.all.min.js"></script>
  <!-- dependency: html2canvas -->
  <script src="html2canvas.min.js"></script>
  <!-- dependency: canvas2image -->
  <script src="canvas2image.js"></script>
  <!-- GlowDemo assets  -->
  <link href="glowDemo.css" rel="stylesheet">
  <script src="glowDemo.js"></script>
  <!-- End GlowDemo Assets -->

</head>
<body>
  <div class="TitlePanel">
    <div class="Title">
      <h1>Glow: Better Reversible Generative Models</h1>
      <time>June 30, 2018</time>
    </div>
  </div>
  <div class="Content">
    <p class="MockUpNotice">
      This mock-up mimics the look of the in-progress article to inform a design
      that embeds the demo into the article. The relevant assets just need to be
      migrated into the final article.
    </p>

    <p>We’ve developed Glow, a new type of generative model which uses
      invertible 1x1 convolutions to create rich, synthetic models of data,
      automatically discovering features we can manipulate. The model extends
      previous work on reversible generative models, simplifying the
      architecture and leading to substantially better results. We’re releasing
      code for the model and an online visualization tool so people can explore
      and build on these results.</p>

    <a>Read Paper</a><br>
    <a>View Code</a>

    <!-- Begin Glow Demo -->

    <div class="GlowDemo_Container">
    </div>

    <!-- End Glow Demo -->

    <h1>Motivation</h1>
    <p>Generative modeling is about observing data, like a set of pictures of
      faces, then learning a model of how this data was generated. Learning to
      approximate the data-generating process requires learning all structure
      present in the data, and successful models should be able to synthesize
      outputs that look similar to the data. Accurate generative models have
      broad applications, including speech synthesis, text analysis and
      synthesis, semi-supervised learning and model-based control. The technique
      we propose can be applied to those problems as well.</p>
  </div>
</body>
</html>